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The ideal candidate should have an outstanding scientific reputation in the field of machine learning research for computational biology and a demonstrated passion for solving biological problems relevant to drug discovery.
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Degree in Computer Science, Robotics, Electrical Engineering, or a related field, with a focus on artificial intelligence, machine learning, or robotics. Familiarity with sensor fusion techniques and the integration of data from various sensors such as LiDAR, cameras, radar, and GPS. Expertise in computer vision, natural language processing, or reinforcement learning as applied to autonomous vehicles.
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If the candidate does not have a background in Actuarial science, the candidate should have 4+ years of industry experience building and analyzing machine learning models and should hold a graduate degree in a technical field such as Statistics, Computer Science, Data Science, Bioinformatics, Physics, Mathematics, Economics or Engineering.
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Experience with a wide range of AI techniques, including machine learning, deep learning, natural language processing, and computer vision. Advanced degree (PhD or MS) in Computer Science, AI, Machine Learning, or a related field.
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Utilize advanced data analytics, including predictive modeling, statistical analysis, data mining, and machine learning, to drive decision-making and improve Amazon marketplace strategies.
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We deploy advanced technology, including molecular discovery, synthetic biology, artificial intelligence, machine learning, and computer vision to develop exceptional products and deliver consumers a superior experience.
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The multi-disciplinary team consists of talented technical staff with expertise in electromagnetics, optics, signal processing (communication, GPS), RF design, statistics, optimization, computer vision, machine learning, autonomous control, software engineering, and embedded systems.
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Stay abreast of latest AI trends and maintain state-of-the-art knowledge in machine learning, deep learning, and computational linguistics. PhD in Computer Science, Artificial Intelligence, Computational Linguistics, or a related field.
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Experience in Data Science/cloud-based machine learning platforms (AWS Bedrock, AWS Sagemaker, Snowflake Cortex etc. Bachelor’s degree in computer science, Information Systems, Software Engineering or similar field.
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Work with research informatics and data teams to deploy enterprise-level cheminformatics and machine learning solutions across the discovery and medicinal chemistry groups. This will include implementing machine learning methods for physicochemical property predictions as well as ADME/PK/tox endpoints.
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Enrolled in a degree program in Computer Science, Visual Effects, Graphic Art, Animation, or related field with at least one semester of school left after completion of co-op. Current projects underway in hardware and software include autonomous movement and mobility, artificial intelligence and machine learning, manipulation, simulation, robotic management software, predictive analytics, and much more.
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Design and implement computer algorithms, potentially including supervised and unsupervised machine learning methods, deep learning (autoencoders, transformers, geometric deep learning, dynamical systems, model decomposition), etc.
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PhD degree in Computational Biology, Bioinformatics, Computer Science, or a related field with 7+ years experience , or MS with 13+ years experience, or BS with 15+ years experience. Experience with data visualization tools (e.g., Spotfire) and advanced analytics techniques (e.g., machine learning) is required.
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Ideal candidates will possess: Familiarity with analyzing oncology clinical dataFamiliarity with DBT workflowsFamiliarity with creating and maintaining BI dashboards; Looker experience nice to haveFamiliarity with machine learning techniques, with an understanding of the strengths and limitations of various approachesProficiency in navigating large, complex problems within a fast-paced environment.
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MD with clinical experience (e.g., residency), Ph. D., Masters or Bachelors degree in Medicine, Biotechnology, Statistics, Machine Learning & Artificial Intelligence, Physics, Mathematics, Computational Chemistry, Computational Biology, Biology or a related discipline.
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